




'''simple docstring'''


import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_					=   logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase_					=   []
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (f"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", f"""encoder.layers.{i}.self_attn.out_proj.weight""")
  )
  rename_keys.append(
      (f"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", f"""encoder.layers.{i}.self_attn.out_proj.bias""")
  )
  rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""encoder.layers.{i}.fc1.weight"""))
  rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""encoder.layers.{i}.fc1.bias"""))
  rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""encoder.layers.{i}.fc2.weight"""))
  rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""encoder.layers.{i}.fc2.bias"""))
  rename_keys.append(
      (f"""transformer.encoder.layers.{i}.norm1.weight""", f"""encoder.layers.{i}.self_attn_layer_norm.weight""")
  )
  rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
  rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""encoder.layers.{i}.final_layer_norm.weight"""))
  rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""encoder.layers.{i}.final_layer_norm.bias"""))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""decoder.layers.{i}.self_attn.out_proj.weight""")
  )
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""decoder.layers.{i}.self_attn.out_proj.bias""")
  )
  rename_keys.append(
      (
          f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
          f"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
      )
  )
  rename_keys.append(
      (
          f"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
          f"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
      )
  )
  rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""decoder.layers.{i}.fc1.weight"""))
  rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""decoder.layers.{i}.fc1.bias"""))
  rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""decoder.layers.{i}.fc2.weight"""))
  rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""decoder.layers.{i}.fc2.bias"""))
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.norm1.weight""", f"""decoder.layers.{i}.self_attn_layer_norm.weight""")
  )
  rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.norm2.weight""", f"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
  )
  rename_keys.append(
      (f"""transformer.decoder.layers.{i}.norm2.bias""", f"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
  )
  rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""decoder.layers.{i}.final_layer_norm.weight"""))
  rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""decoder.layers.{i}.final_layer_norm.bias"""))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.encoder.norm.weight", "encoder.layernorm.weight"),
        ("transformer.encoder.norm.bias", "encoder.layernorm.bias"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
    ]
)


def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]					,       a_						:				List[Any]					,       a_						:				Any      ):
  __a								=       state_dict.pop(a_      )
  __a								=       val


def     SCREAMING_SNAKE_CASE							(   a_						:				Dict      ):
  __a								=       OrderedDict()
  for key, value in state_dict.items():
    if "backbone.0.body" in key:
      __a								=       key.replace('backbone.0.body'					,       'backbone.conv_encoder.model'      )
      __a								=       value
    else:
      __a								=       value

  return new_state_dict


def     SCREAMING_SNAKE_CASE							(   a_						:				Dict      ):
  __a								=       ''

  # first: transformer encoder
  for i in range(6      ):
    # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
    __a								=       state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"      )
    __a								=       state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"      )
    # next, add query, keys and values (in that order) to the state dict
    __a								=       in_proj_weight[:256, :]
    __a								=       in_proj_bias[:256]
    __a								=       in_proj_weight[256:512, :]
    __a								=       in_proj_bias[256:512]
    __a								=       in_proj_weight[-256:, :]
    __a								=       in_proj_bias[-256:]
  # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
  for i in range(6      ):
    # read in weights + bias of input projection layer of self-attention
    __a								=       state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"      )
    __a								=       state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"      )
    # next, add query, keys and values (in that order) to the state dict
    __a								=       in_proj_weight[:256, :]
    __a								=       in_proj_bias[:256]
    __a								=       in_proj_weight[256:512, :]
    __a								=       in_proj_bias[256:512]
    __a								=       in_proj_weight[-256:, :]
    __a								=       in_proj_bias[-256:]
    # read in weights + bias of input projection layer of cross-attention
    __a								=       state_dict.pop(
        f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"      )
    __a								=       state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"      )
    # next, add query, keys and values (in that order) of cross-attention to the state dict
    __a								=       in_proj_weight_cross_attn[:256, :]
    __a								=       in_proj_bias_cross_attn[:256]
    __a								=       in_proj_weight_cross_attn[256:512, :]
    __a								=       in_proj_bias_cross_attn[256:512]
    __a								=       in_proj_weight_cross_attn[-256:, :]
    __a								=       in_proj_bias_cross_attn[-256:]


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				Tuple      ):
  __a						,							__a								=       image.size
  __a								=       max(a_					,       a_      )
  __a								=       800 if 'detection' in checkpoint_url else 1000
  __a								=       target_max_size / current_max_size
  __a								=       image.resize((int(round(scale * width      )      ), int(round(scale * height      )      ))      )

  return resized_image


def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[Any]      ):
  __a								=       F.to_tensor(a_      )
  __a								=       F.normalize(a_					,       mean=[0.4_85, 0.4_56, 0.4_06]					,       std=[0.2_29, 0.2_24, 0.2_25]      )
  return image


@torch.no_grad()
def     SCREAMING_SNAKE_CASE							(   a_						:				Union[str, Any]					,       a_						:				List[str]					,       a_						:				Union[str, Any]      ):
  logger.info('Converting model...'      )

  # load original state dict
  __a								=       torch.hub.load_state_dict_from_url(a_					,       map_location='cpu'      )
  # rename keys
  for src, dest in rename_keys:
    rename_key(a_					,       a_					,       a_      )
  __a								=       rename_backbone_keys(a_      )
  # query, key and value matrices need special treatment
  read_in_q_k_v(a_      )
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  __a								=       'model.'
  for key in state_dict.copy().keys():
    if not key.startswith('class_labels_classifier'      ) and not key.startswith('bbox_predictor'      ):
      __a								=       state_dict.pop(a_      )
      __a								=       val
    # create HuggingFace model and load state dict
  __a								=       TableTransformerConfig(
      backbone='resnet18'					,       mask_loss_coefficient=1					,       dice_loss_coefficient=1					,       ce_loss_coefficient=1					,       bbox_loss_coefficient=5					,       giou_loss_coefficient=2					,       eos_coefficient=0.4					,       class_cost=1					,       bbox_cost=5					,       giou_cost=2					,       )

  if "detection" in checkpoint_url:
    __a								=       15
    __a								=       2
    __a								=       {0: 'table', 1: 'table rotated'}
    __a								=       idalabel
    __a								=       {v: k for k, v in idalabel.items()}
  else:
    __a								=       125
    __a								=       6
    __a								=       {
        0: 'table',
        1: 'table column',
        2: 'table row',
        3: 'table column header',
        4: 'table projected row header',
        5: 'table spanning cell',
    }
    __a								=       idalabel
    __a								=       {v: k for k, v in idalabel.items()}

  __a								=       DetrImageProcessor(
      format='coco_detection'					,       max_size=800 if 'detection' in checkpoint_url else 1000      )
  __a								=       TableTransformerForObjectDetection(a_      )
  model.load_state_dict(a_      )
  model.eval()

  # verify our conversion
  __a								=       'example_pdf.png' if 'detection' in checkpoint_url else 'example_table.png'
  __a								=       hf_hub_download(repo_id='nielsr/example-pdf'					,       repo_type='dataset'					,       filename=a_      )
  __a								=       Image.open(a_      ).convert('RGB'      )
  __a								=       normalize(resize(a_					,       a_      )      ).unsqueeze(0      )

  __a								=       model(a_      )

  if "detection" in checkpoint_url:
    __a								=       (1, 15, 3)
    __a								=       torch.tensor(
        [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]]      )
    __a								=       torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]]      )

  else:
    __a								=       (1, 125, 7)
    __a								=       torch.tensor(
        [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]]      )
    __a								=       torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]]      )

  assert outputs.logits.shape == expected_shape
  assert torch.allclose(outputs.logits[0, :3, :3]					,       a_					,       atol=1e-4      )
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,       a_					,       atol=1e-4      )
  print('Looks ok!'      )

  if pytorch_dump_folder_path is not None:
    # Save model and image processor
    logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."      )
    Path(a_      ).mkdir(exist_ok=a_      )
    model.save_pretrained(a_      )
    image_processor.save_pretrained(a_      )

  if push_to_hub:
    # Push model to HF hub
    logger.info('Pushing model to the hub...'      )
    __a								=       (
        'microsoft/table-transformer-detection'
        if 'detection' in checkpoint_url
        else 'microsoft/table-transformer-structure-recognition'
    )
    model.push_to_hub(a_      )
    image_processor.push_to_hub(a_      )


if __name__ == "__main__":
  UpperCAmelCase_					=   argparse.ArgumentParser()

  parser.add_argument(
      "--checkpoint_url",
      default="https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
      type=str,
      choices=[
          "https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth",
          "https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth",
      ],
      help="URL of the Table Transformer checkpoint you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )
  UpperCAmelCase_					=   parser.parse_args()
  convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''


from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
UpperCAmelCase_					=   Lock()


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				Tuple					,       a_						:				Dict					,       a_						:				Optional[Any]					,       a_						:				str					,       a_						:				Any					,       a_						:				Optional[Any]      ):
  global process_lock

  # we perform n swaps since after n swaps we know we are sorted
  # we *could* stop early if we are sorted already, but it takes as long to
  # find out we are sorted as it does to sort the list with this algorithm
  for i in range(0					,       10      ):
    if (i + position) % 2 == 0 and r_send is not None:
      # send your value to your right neighbor
      process_lock.acquire()
      r_send[1].send(a_      )
      process_lock.release()

      # receive your right neighbor's value
      process_lock.acquire()
      __a								=       rr_cv[0].recv()
      process_lock.release()

      # take the lower value since you are on the left
      __a								=       min(a_					,       a_      )
    elif (i + position) % 2 != 0 and l_send is not None:
      # send your value to your left neighbor
      process_lock.acquire()
      l_send[1].send(a_      )
      process_lock.release()

      # receive your left neighbor's value
      process_lock.acquire()
      __a								=       lr_cv[0].recv()
      process_lock.release()

      # take the higher value since you are on the right
      __a								=       max(a_					,       a_      )
    # after all swaps are performed, send the values back to main
  result_pipe[1].send(a_      )


def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]      ):
  __a								=       []
  __a								=       []
  # initialize the list of pipes where the values will be retrieved
  for _ in arr:
    result_pipe.append(Pipe()      )
  # creates the processes
  # the first and last process only have one neighbor so they are made outside
  # of the loop
  __a								=       Pipe()
  __a								=       Pipe()
  process_array_.append(
      Process(
          target=a_					,       args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])					,       )      )
  __a								=       temp_rs
  __a								=       temp_rr

  for i in range(1					,       len(a_      ) - 1      ):
    __a								=       Pipe()
    __a								=       Pipe()
    process_array_.append(
        Process(
            target=a_					,       args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])					,       )      )
    __a								=       temp_rs
    __a								=       temp_rr

  process_array_.append(
      Process(
          target=a_					,       args=(
              len(a_      ) - 1,
              arr[len(a_      ) - 1],
              temp_ls,
              None,
              temp_lr,
              None,
              result_pipe[len(a_      ) - 1],
          )					,       )      )

  # start the processes
  for p in process_array_:
    p.start()

  # wait for the processes to end and write their values to the list
  for p in range(0					,       len(a_      )      ):
    __a								=       result_pipe[p][0].recv()
    process_array_[p].join()
  return arr


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       list(range(10					,       0					,       -1      )      )
  print('Initial List'      )
  print(*a_      )
  __a								=       odd_even_transposition(a_      )
  print('Sorted List\n'      )
  print(*a_      )


if __name__ == "__main__":
  main()



